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Recently, it has received much research attention due to its wide application in various fields. This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization. First, the patch features of normal images are extracted by a deep network pre-trained on nature images. Then, the prototypes of the normal patch features are learned by non-parametric clustering. Finally, we construct an image anomaly localization network (ProtoAD) by appending the feature extraction network with\n                    <jats:italic>L<\/jats:italic>\n                    2 feature normalization, a\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$1\\times 1$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>1<\/mml:mn>\n                            <mml:mo>\u00d7<\/mml:mo>\n                            <mml:mn>1<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    convolutional layer, a channel max-pooling, and a subtraction operation. We use the prototypes as the kernels of the\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$1\\times 1$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>1<\/mml:mn>\n                            <mml:mo>\u00d7<\/mml:mo>\n                            <mml:mn>1<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    convolutional layer; therefore, our neural network does not need a training phase and can conduct anomaly detection and localization in an end-to-end manner. Extensive experiments on two challenging industrial anomaly detection datasets, MVTec AD and BTAD, demonstrate that ProtoAD achieves competitive performance compared to the state-of-the-art methods with a higher inference speed. The code and pre-trained models are publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/98chao\/ProtoAD\">https:\/\/github.com\/98chao\/ProtoAD<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s11063-024-11466-7","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T14:02:17Z","timestamp":1715176937000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Prototype-Based Neural Network for Image Anomaly Detection and Localization"],"prefix":"10.1007","volume":"56","author":[{"given":"Chao","family":"Huang","sequence":"first","affiliation":[]},{"given":"Zhao","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"issue":"3","key":"11466_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. 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